Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy

Hassan, Bilal; Qin, Shiyin; Ahmed, Ramsha; Hassan, Taimur; Taguri, Abdel Hakeem; Hashmi, Shahrukh K.; Werghi, Naoufel

doi:10.1016/j.compbiomed.2021.104727

Cited by 58 publications

(25 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Contrary to the order, however, we can observe a dependency solely due to the characteristics of the dataset. The related works that used the publicly available RETOUCH 47 test dataset all report relatively small differences in Dice score between the classes with a std of 0.020 to 0.025 [48][49][50][51] while the ones using private datasets report larger values of 0.075 52 and 0.087 53 . Consequently, we assign the reason for the spread of the Dice scores between the classes primarily to the characteristic of the dataset.…”

Section: Class-wise Performancementioning

confidence: 98%

A Vision Transformer Architecture for the Automated Segmentation of Retinal Fluids in Spectral Domain Optical Coherence Tomography Images

Philippi

Rothaus

Castelli

2022

Preprint

View full text Add to dashboard Cite

Neovascular age-related macular degeneration (nAMD) is one of the major causes of irreversible blindness and is characterized by accumulations of different fluids inside the retina. An early detection and activity monitoring of predominately three types of fluids, namely intra-retinal fluid (IRF), sub-retinal fluid (SRF), and pigment epithelium detachment (PED), is critical for a successful treatment. Spectral-domain optical coherence tomography (SD-OCT) revolutionized nAMD treatment by providing cross-sectional, high-resolution images of the retina. Automatic segmentation and quantification of IRF, SRF, and PED in SD-OCT images can be extremely useful for clinical decision-making. Despite the use of state-of-the-art convolutional neural network (CNN)-based methods, the task remains challenging due to relevant variations in the location, size, shape, and texture of the fluids. This work is the first to adopt a transformer-based method to automatically segment retinal fluid from SD-OCT images and qualitatively and quantitatively evaluate its performance against CNN-based methods. The method combines the efficient long-range feature extraction and aggregation capabilities of Vision Transformers (ViTs) with data-efficient training of CNNs. The proposed method was tested on a private dataset containing 3842 2-dimensional SD-OCT retina images, manually labeled by experts of the Franziskus-Eye-Hospital. While one of the competitors presents a better performance in terms of Dice score, the proposed method is significantly less computationally expensive. Thus, future research will focus on the proposed network's architecture to increase its segmentation performance while maintaining its computational efficiency.

show abstract

Section: Class-wise Performancementioning

confidence: 98%

A Vision Transformer Architecture for the Automated Segmentation of Retinal Fluids in Spectral Domain Optical Coherence Tomography Images

Philippi

Rothaus

Castelli

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In the AUnet decoder, every step consists of two 3 × 3 convolutions, each followed by a ReLU, concatenated with the cropped feature map from the corresponding contracting path. Next, the feature maps are upsampled using a 2 × 2 transposed convolution [15,16] that halves the number of feature channels. The cropping operation is required since each convolution operation results in a loss of border pixels.…”

Section: Aunet Decodermentioning

confidence: 99%

“…Recent advancements in ANN research have demonstrated the efficacy of deep learning methods in handling various segmentation [15][16][17], detection [18][19][20][21], and classification [22,23] tasks. In particular, the use of convolutional neural networks (CNNs) has resulted in substantial progress in image recognition [24].…”

Section: Introductionmentioning

confidence: 99%

AUnet: A Deep Learning Framework for Surface Water Channel Mapping Using Large-Coverage Remote Sensing Images and Sparse Scribble Annotations from OSM Data

et al. 2022

Self Cite

View full text Add to dashboard Cite

Water is a vital component of life that exists in a variety of forms, including oceans, rivers, ponds, streams, and canals. The automated methods for detecting, segmenting, and mapping surface water have improved significantly with the advancements in satellite imagery and remote sensing. Many strategies and techniques to segment water resources have been presented in the past. However, due to the variant width and complex appearance, the segmentation of the water channel remains challenging. Moreover, traditional supervised deep learning frameworks have been restricted by the scarcity of water channel datasets that include precise water annotations. With this in mind, this research presents the following three main contributions. Firstly, we curated a new dataset for water channel mapping in the Pakistani region. Instead of employing pixel-level water channel annotations, we used a weakly trained method to extract water channels from VHR pictures, relying only on OpenStreetMap (OSM) waterways to create sparse scribbling annotations. Secondly, we benchmarked the dataset on state-of-the-art semantic segmentation frameworks. We also proposed AUnet, an atrous convolution inspired deep learning network for precise water channel segmentation. The experimental results demonstrate the superior performance of the proposed AUnet model for segmenting using weakly supervised labels, where it achieved a mean intersection over union score of 0.8791 and outperformed state-of-the-art approaches by 5.90% for the extraction of water channels.

show abstract

“…Artificial intelligence (AI) technology has risen for an automated surveillance system. While discussing AI technologies in surveillance systems, one cannot ignore the advancements in machine learning algorithms for detection and recognition [5,6], which has developed to be the integral part of computer vision technology [7,8]. Researchers' interest in machine learning has recently increased in other domains as well [9][10][11] due to the development of inexpensive data storage technology and high-performing GPUs.…”

Section: Introductionmentioning

confidence: 99%

DeepFire: A Novel Dataset and Deep Transfer Learning Benchmark for Forest Fire Detection

Khan

Hassan

Khan

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

Forest fires pose a potential threat to the ecological and environmental systems and natural resources, impacting human lives. However, automated surveillance system for early forest fire detection can mitigate such calamities and protect the environment. Therefore, we propose a UAV-based forest fire fighting system with integrated artificial intelligence (AI) capabilities for continuous forest surveillance and fire detection. The major contributions of the proposed research are fourfold. Firstly, we explain the detailed working mechanism along with the key steps involved in executing the UAV-based forest fire fighting system. Besides, a robust forest fire detection system requires precise and efficient classification of forest fire imagery against no-fire. Moreover, we have curated a novel dataset (DeepFire) containing diversified real-world forest imagery with and without fire to assist future research in this domain. The DeepFire dataset consists of 1900 colored images with 950 each for fire and no-fire classes. Next, we investigate the performance of various supervised machine learning classifiers for the binary classification problem of detecting forest fire. Furthermore, we propose a VGG19-based transfer learning solution to achieve improved prediction accuracy. We assess and compare the performance of several machine learning approaches such as k -nearest neighbors, random forest, naive Bayes, support vector machine, logistic regression, and the proposed approach for accurately identifying fire and no-fire images in the DeepFire dataset. The simulation results demonstrate the efficacy of the proposed approach in terms of accurate classification, where it achieves the mean accuracy of 95% with 95.7% precision and 94.2% recall.

show abstract

Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy

Cited by 58 publications

References 53 publications

A Vision Transformer Architecture for the Automated Segmentation of Retinal Fluids in Spectral Domain Optical Coherence Tomography Images

A Vision Transformer Architecture for the Automated Segmentation of Retinal Fluids in Spectral Domain Optical Coherence Tomography Images

AUnet: A Deep Learning Framework for Surface Water Channel Mapping Using Large-Coverage Remote Sensing Images and Sparse Scribble Annotations from OSM Data

DeepFire: A Novel Dataset and Deep Transfer Learning Benchmark for Forest Fire Detection

Contact Info

Product

Resources

About